A genome-wide interaction analysis of tri/tetracyclic antidepressants and RR and QT

intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research

in Genomic Epidemiology (CHARGE) consortium

Raymond Noordam1, 2, *, Colleen M Sitlani3, *, Christy L Avery4, *, James D Stewart4, 5,

Stephanie M Gogarten6, Kerri L Wiggins3, Stella Trompet2, 7, Helen R Warren8, 9, Fangui

Sun10, Daniel S Evans11, Xiaohui Li12, Jin Li13, Albert V Smith14, 15, Joshua C Bis3, Jennifer A

Brody3, Evan L Busch16, 17, Mark J Caulfield8, 9, Yii-Der I Chen12, Steven R Cummings11, L

Adrienne Cupples10, 18, Qing Duan19, Oscar H Franco1, Rául Méndez-Giráldez4, Tamara B

Harris20, Susan R Heckbert21, Diana van Heemst2, Albert Hofman1, 16, James S Floyd3, 21, Jan A

Kors22, Lenore J Launer20, Yun Li19, 23, 24, Ruifang Li-Gao25, Leslie A Lange19, Henry J Lin12, 26,

Renée de Mutsert25, Melanie D Napier4, Christopher Newton-Cheh18, 27, 28, Neil Poulter29,

Alexander P Reiner21, 30, Kenneth M Rice6, Jeffrey Roach31, Carlos J Rodriguez32, 33, Frits R

Rosendaal25, Naveed Sattar34, Peter Sever29, Amanda A Seyerle4, P Eline Slagboom35, Elsayed

Z Soliman36, Nona Sotoodehnia3, 21, David J Stott37, Til Stürmer4, 38, Kent D Taylor12, Timothy

A Thornton6, André G Uitterlinden39, Kirk C Wilhelmsen19, 40, James G Wilson41, Vilmundur

Gudnason14, 15, J Wouter Jukema7, 42, 43, Cathy C Laurie6, Yongmei Liu44, Dennis O Mook-

Kanamori25, 45, 46, Patricia B Munroe8, 9, Jerome I Rotter12, Ramachandran S Vasan18, 47, Bruce

M Psaty3, 21, 48, 49, †, Bruno H Stricker1, 50, †, Eric A Whitsel4, 51, †

*) These authors contributed equally to this work.

†) These authors jointly directed this work.

Author affiliations 1. Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands.

2. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the

Netherlands.

3. Department of Medicine, University of Washington, Seattle, WA, USA.

4. Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.

5. Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA.

6. Department of Biostatistics, University of Washington, Seattle, WA, USA.

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7. Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.

8. Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine, Queen Mary University

of London, London, UK.

9. NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine, Queen Mary University of

London, London, UK.

10. Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA.

11. California Pacific Medical Center Research Institute, San Francisco, CA, USA.

12. Institute for Translational Genomics and Population Sciences and Department of Pediatrics, Los Angeles Biomedical Research

Institute at Harbor-UCLA Medical Center, Torrance, California, USA.

13. Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA.

14. Icelandic Heart Association, Kopavogur, Iceland.

15. Faculty of Medicine, University of Iceland, Reykavik, Iceland.

16. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

17. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical

School, Boston, MA, USA.

18. Framingham Heart Study, Framingham, MA, USA.

19. Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.

20. Laboratory of Epidemiology, Demography, and Biometry, National Institue on Aging, Bethesda, MD, USA.

21. Department of Epidemiology, University of Washington, Seattle, WA, USA.

22. Department of Medical Informatics, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands.

23. Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.

24. Department of Computer Science, University of North Carolina, NC, USA.

25. Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.

26. Division of Medical Genetics, Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, California, USA.

27. Cardiovascular Research Center & Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA.

28. Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.

29. International Centre for Circulatory Health, Imperial College London, W2 1PG, UK.

30. Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

31. Research Computing Center, University of North Carolina, Chapel Hill, NC, USA.

32. Department of Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA.

33. Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.

34. BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom.

35. Department of Medical Statistics and Bioinformatics, Section of Molecular Epidemiology, Leiden University Medical Center,

Leiden, the Netherlands.

36. Epidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC, USA.

37. Institute of Cardiovascular and Medical Sciences, University of Glasgow, United Kingdom.

38. Pharmacoepidemiology, University of North Carolina, Chapel Hill, NC, USA.

39. Department of Internal Medicine, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands.

40. The Renaissance Computing Institute, Chapel Hill, NC, USA.

41. Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA.

42. Durrer Center for Cardiogenetic Research, Amsterdam, the Netherlands.

43. Interuniversity Cardiology Institute of the Netherlands, Utrecht, the Netherlands.

44. Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC,

USA.

45. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands.

46. Department of BESC, Epidemiology Section, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.

47. Department of Medicine, School of Medicine, Boston University, Boston, MA, USA.

48. Department of Health Services, University of Washington, Seattle, WA, USA.

49. Group Health Research Institue, Group Health Cooperative, Seattle, WA, USA.

50. Inspectorate of Health Care, Utrecht, the Netherlands.

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51. Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.

Address of correspondence

Raymond Noordam PhD

Department of Internal Medicine, Section of Gerontology and Geriatrics,

Leiden University Medical Center

P.O. Box 9600, 2300 RC Leiden, the Netherlands

Email: r.noordam@lumc.nl

Bruno H Stricker Mmed PhD

Department of Epidemiology

Erasmus MC – University Medical Center Rotterdam,

P.O. Box 2040, 3000 CA, Rotterdam, the Netherlands

Email: b.stricker@erasmusmc.nl

Running title: “SNP-by-TCA interactions on RR and QT intervals”

Key words: drug-gene interaction, Genome Wide Association Study, tri/tetracyclic

antidepressants, RR interval, QT interval electrocardiography

Number of words abstract: 248

Number of words manuscript: 3,426

Number of tables in manuscript: 2

Number of figures in manuscript: 2

Number of references in manuscript: 44

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Abstract

Background: Increased heart rate and a prolonged QT interval are important risk factors for

cardiovascular morbidity and mortality, and can be influenced by the use of various

medications, including tri/tetracyclic antidepressants (TCAs). We aim to identify genetic loci

that modify the association between TCA use and RR and QT intervals.

Methods and Results: We conducted race/ethnic-specific genome-wide interaction analyses

(with HapMap Phase II imputed reference panel imputation) of TCAs and resting RR and QT

intervals in cohorts of European (n=45,706; n=1,417 TCA users), African (n=10,235; n=296

TCA users) and Hispanic/Latino (n=13,808; n=147 TCA users) ancestry, adjusted for clinical

covariates. Among the populations of European ancestry, two genome-wide significant loci

were identified for RR interval: rs6737205 in BRE (β = 56.3, Pinteraction = 3.9e-9) and rs9830388

in UBE2E2 (β = 25.2, Pinteraction = 1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3

significantly modified the association between TCAs and QT intervals (β = 9.3, Pinteraction =

2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the

meta-analyses (as part of quality control), or their effects did not reach the level of nominal

statistical significance (Pinteraction > 0.05). No new variants were identified in these ethnicities.

No additional loci were identified after inverse-variance-weighted meta-analysis of the three

ancestries.

Conclusion: Among Europeans, TCA interactions with variants in BRE and UBE2E2, were

identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified

the relation between TCAs and QT intervals. Future studies are required to confirm our

results.

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Introduction

An increased resting heart rate and a prolonged QT interval are independent risk

factors for cardiovascular morbidity and mortality[1-4]. To date, multiple medications have

shown clinically significant effects on heart rate, the heart-rate corrected QT interval (QTc),

or both[5-7]. For example, the tri/tetracyclic antidepressants (TCAs) have tachycardic and

QT-prolonging effects originating from their anticholinergic properties (through antagonizing

acetylcholine neurotransmitter signaling[5 7-11]). Despite drug safety warnings, particularly

in at risk populations (e.g., the elderly), TCAs are still commonly prescribed in Western

societies[12-14] for the treatment of depression, anxiety, insomnia, and neuropathic pain[12].

Both resting heart rate and QT interval duration are heritable, with hereditability

estimates ranging from 55-77% for resting heart rate and 35-51% for QT intervals[15 16]. To

date, multiple single nucleotide polymorphisms (SNPs) have been identified in genome-wide

association studies of resting heart rate[17-19] and QT interval[20 21] among different

ethnicities. However, the identified loci (21 for resting heart rate and 35 for QT interval

duration[17 20]) explain only 0.8-0.9% and 8-10% of the total variance in these traits[17 20].

Inability to fully explain variance in heart rate and QT intervals may be related to the

presence of gene-gene and gene-environment interactions[22]. To examine this possibility, a

genome-wide, TCA-SNP interaction meta-analysis of QT was previously conducted in

individuals of European ancestry within the Cohorts for Heart and Aging Research in

Genomic Epidemiology (CHARGE) consortium[23]. However, no significant TCA-SNP

interactions were identified, possibly due to the small number of TCA users in the study or its

cross-sectional design[23]. Since then, new statistical methods have been developed to

incorporate data from multiple visits[24], and additional cohorts of different ancestral origins

have been included to increase statistical power.

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The present effort collaboratively leverages these methods in a study designed to

identify TCA-SNP interactions capable of explaining variation in heart rate (or RR interval)

and QT, while also providing insights into the biology of tachycardic and QT-prolonging

medications.

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Methods

Study populations

The present study used data from 21 different cohorts of three ancestral populations

(European [14 cohorts], African [5 cohorts], and Hispanic/Latino [2 cohorts, noting that

“Hispanic/Latino” captures a diverse population][25]) that were assembled and analyzed by

the Pharmacogenomics Working Group in the CHARGE consortium[26]. All cohorts

conducted the analyses within their own study on the basis of a predefined protocol. Cohorts

with genetic data were eligible to participate when data on medication use and on the study

outcomes were both collected during the same visit. Genotype data had to be imputed with

either the HapMap Phase 2[27] or 1000 Genomes reference panel[28]. One of the studies

(Anglo-Scandinavian Cardiac Outcomes Trial [ASCOT]) did not record electrocardiograms

(ECGs), and therefore participated in analyses on only RR, and not on QT. All studies were

approved by local ethics committees, and all participants gave written informed consent.

Cohort-specific descriptions of the study design can be found in the Supplementary

Materials.

Inclusion and exclusion criteria

All participants with data on medication use and a high quality ECG (when available),

and who were successfully genotyped, were eligible for inclusion in the analyses. Participants

with atrial fibrillation, a pacemaker, and/or second or third degree atrioventricular block were

excluded from the analyses, as were participants with heart failure or a QRS duration ≥120

milliseconds (ms).

Drug exposure assessment

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Most cohorts collected information on medication use by inventory (Supplementary

Table 1). However, the Rotterdam Study (RS) defined medication use on the basis of

pharmacy dispensing data (from 1991 onwards). For these individuals, exposure was defined

as a prescription filled for a medication of interest within 30 days preceding the ECG

recording. Cohorts were asked to define exposures to the following medications (or

medication classes): TCAs (ATC code “N06AA”), beta-blocking agents (ATC code “C07”),

verapamil (ATC code “C08DA01”), diltiazem (ATC code “C08DB01”), and medications

known to definitely prolong QT intervals or that are generally accepted to increase the risk of

torsade de pointes. Categorization of medications as “definite” for QT prolongation was

based on classification from the Arizona Center for Education and Research on Therapeutics

(UAZ CERT) as of March 2008[29].

Assessment of QT and RR interval

In each cohort, research technicians recorded a standard 12-lead ECG or pulse rate (in

the case of ASCOT) in the resting state for each participant (Supplementary Table 2).

Almost all cohorts measured RR and QT intervals automatically, to decrease measurement

error and inter-individual variation. Studies conducted all analyses longitudinally, allowing

multiple visits per participant in the analyses when multiple ECGs were available (and when

data on medication use were also collected).

Genotyping and imputation

Genome-wide SNP genotyping was performed within each cohort separately, using

commercially available genotyping arrays from Affymetrix (Santa Clara, CA, USA) or

Illumina (San Diego, CA, USA; Supplementary Table 3). Duplicates and samples with

gender mismatches were excluded from all studies. First-degree relatives were excluded from

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all studies, except for the family-based Framingham Heart Study (FHS), Jackson Heart Study

(JHS), and Hispanic Community Health Study/Study of Latinos (HCHS/SOL); HCHS/SOL

investigators also used methods that accounted for admixture, population structure, and

Hardy-Weinberg-departures, when estimating kinship coefficients[25]. Cohort-specific

thresholds for genotyping call rates ranged from 95% to 99%. To increase homogeneity

between cohorts with respect to the SNPs genotyped by the different platforms, as well as to

increase coverage, summary results were based on SNPs from the HapMap Phase 2 (build

36) reference population[27], given the uniform availability of HapMap2-imputed SNPs and

the computational burdens associated with performing analyses for reference panels with

much larger numbers of SNPs.

Genome-wide TCA-SNP interaction analyses and meta-analysis

The statistical approaches used to estimate TCA-SNP interactions on RR or QT

intervals depended on the study design (e.g., family-based) and the availability of ECG and

medication data (e.g., cross-sectional or longitudinal). Cohorts with longitudinal ECG and

medication data (e.g., Atherosclerosis Risk in Communities Study, Cardiovascular Health

Study, RS, and Women’s Health Initiative [WHI]) used generalized estimating equations

(GEE)[30] with independent working correlation structure. The family-based FHS and

HCHS/SOL studies used linear mixed models that accounted for relatedness, sampling design

(HCHS/SOL), and heterogeneity of outcome variance by drug use (HCHS/SOL). Cohorts

with unrelated participants and with cross-sectional assessment of ECG and drug data used

linear regression models with robust standard errors, as implemented in the ProbABEL

software package[31] or in the “bosswithDF” package as implemented in the R statistical

environment. Assuming that exposure to TCAs varies randomly across within-person visits

for the analyses on RR, we had a power of 0.91 to observe interaction effects of at least 35

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milliseconds for variants with a minor allele frequency (MAF) of at least 0.25

(Supplementary Table 4). For QT, we had a power 0.91 to observe interaction effects of at

least 7 milliseconds for a MAF of at least 0.25.

TCA-SNP interaction analyses on both RR and QT were adjusted for age and sex.

The analyses of RR were additionally adjusted for the use of beta-blocking agents, verapamil,

and diltiazem. Similarly, analyses of QT were additionally adjusted for the use of

medications that definitely prolong the QT interval and for the resting RR interval. Studies

also adjusted for study-specific covariates, as necessary (e.g., study site and principal

components).

The robust standard error estimates led to inflated type I errors when the number of

participants exposed to the drug and the MAF were both small[24]. We addressed this

potential for false-positive results by incorporating variability in the standard error estimates,

through use of a t-reference distribution with degrees of freedom approximated via

Satterthwaite’s methods[32 33]. However, at the lowest combinations of minor allele

frequency and use of TCAs, the variability of the standard errors was poorly estimated,

requiring exclusion of SNPs where 2*(number of exposed participants)*MAF*imputation

quality < 10, as described previously [24]. An inverse-variance-weighted meta-analysis was

then performed with genomic control using METAL, to combine the results from the

different studies [34]. To avoid high type I errors from robust standard error estimates,

standard errors were “corrected” using the t-distribution-based P-values. These “corrected”

standard error estimates were used as inputs for the inverse-variance-weighted meta-analysis.

Meta-analyses were performed for each ethnic group separately and for all ethnic groups

together. To be considered in our study, SNPs had to be present, after quality control, in at

least three cohorts (two cohorts in case of the Hispanic/Latino meta-analysis).

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A two-sided P-value <5e-8 for TCA-SNP interactions was considered statistically

significant in the genome-wide association analyses. Detailed summary results of the ethnic-

specific analyses (including rs numbers, MAF values, effect sizes, and P-value) are available

through dbGaP (https://www.ncbi.nlm.nih.gov/gap).

Evaluation of previously identified SNPs associated with resting heart rate and QT intervals

Within our European ancestry meta-analysis, we evaluated SNPs that were previously

found to have main effects on heart rate or QT in the GWAS of European ancestry as done

with HapMap Phase II imputed reference panel imputation [17 20]. From the European

GWAS meta-analysis, we extracted all SNPs that had statistically significant effects on heart

rate or QT interval (P-value <5e-8) and were present in at least three cohorts (after all quality

control steps). We adjusted the P-value threshold for statistical significance using the

Bonferroni correction: 2.38e-3 for RR intervals (21 independent loci) and 1.43e-3 for QT

intervals (35 independent loci).

The 21 SNPs associated with RR intervals and the 35 SNPs associated with QT

intervals from the meta-analysis in Europeans were further used to calculate a combined

multi-locus effect estimate on the TCA-SNP interaction. The resulting multi-locus effect can

be interpreted as a Mendelian randomization analysis to assess whether a high resting heart

rate and prolonged QT interval are causal effect modifiers of TCA-induced increases in heart

rate or QT intervals[35]. This data-driven inverse-variance weighted approach[36] has been

implemented in the “gtx” statistical package in the R statistical software environment[37].

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Results

Study characteristics

The number of TCA users for each ethnic group were: Europeans, 1,417 (out of

45,706); African Americans, 295 (out of 10,235); and Hispanics/Latinos, 174 (out of 13,808)

(Table 1). Cohorts had a mean age ranging from 40.2 (FHS) to 75.3 (Prospective Study of

Pravastatin in the Elderly at Risk), and the percentage of included women ranged from 17.8%

(ASCOT) to 100% (WHI). Mean RR intervals ranged from 875 (RS1) to 981 (FHS) ms, and

QT intervals ranged from 397 (RS1) to 416 (HCHS/SOL) ms.

Genome-wide interaction analysis between tricyclic antidepressants and RR intervals

Within the cohorts of European ancestry, two independent loci reached statistical

significance (Table 2; Figure 1A). A q-q plot of the meta-analysis in European cohorts is

presented in Figure 1B. The top independent loci comprised the rs6737205 polymorphism on

chromosome 2 (Figure 1C), and the rs9830388 polymorphism on chromosome 3 (Figure

1D). Variant rs6737205 (passed quality control in four European cohorts) mapped within the

BRE gene and was associated with a 56.3 ms prolongation of the RR interval in TCA users,

beyond the difference attributed to the allele among nonusers (Effect allele frequency [EAF]:

0.94; P-value = 7.66e-9). Variant rs9830388 (passed quality control in all European cohorts),

mapped within the UBE2E2 gene, and was associated with a 25.2 ms longer RR interval in

TCA users, beyond the difference attributed to the allele among nonusers (EAF: 0.51; P-

value = 1.72e-8). Furthermore, rs11877129 (passed quality control in 11 studies) mapped

within the ABCA3 gene and was suggestively (P-value < 1e-7) associated with a 36.7 ms

longer RR interval in TCA users, beyond the difference attributed to the allele among

nonusers (EAF: 0.09; P-value = 2.57e-7). There was no significant heterogeneity between

studies in the observed estimates (P-values > 0.05). These three SNPs, however, did not reach

12

nominal statistical significance in the meta-analyses of the African American cohorts and the

Hispanic/Latino cohorts (Table 2, Supplementary Figure 1). Regional plots are presented in

Supplementary Figure 2. A meta-analysis of the three ethnicities together did not yield any

additional loci with statistically significant effects (Supplementary Figure 3).

Genome-wide interaction analysis between tricyclic antidepressants and QT intervals

Results of the QT meta-analysis in the cohorts of European ancestry are presented in

Figure 2. Within this analysis, no significant TCA-SNP interactions were observed. There

was one locus in the Hispanic/Latino meta-analysis that reached genome-wide significance,

represented by variant rs2291477 (which mapped to TGFBR3; β = 9.3; EAF: 0.88; P-value =

2.55e-8; Supplementary Figure 4/Supplementary Table 5). However, effects of this locus

either did not reach nominal statistical significance or did not pass quality control in the

European and African American meta-analyses (P-values > 0.05). Furthermore, no genome-

wide significant TCA-SNP interactions were observed in the meta-analysis of the three

ethnicities together (Supplementary Figure 5).

Previously identified loci for heart rate and QT intervals

The TCA-SNP interactions on RR and QT for SNPs that were previously associated

with heart rate and QT are presented in Supplementary Tables 6 and 7. None of the loci

previously associated with heart rate or QT showed TCA-SNP interactions on RR or QT

respectively (after Bonferroni correction for the number of SNPs included). Furthermore,

multi-locus effects of all loci for RR and QT were not statistically significant

(Supplementary Figures 6 and 7; P-values = 0.35 and 0.74 for RR and QT, respectively).

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Discussion

In a study population of 45,706 European individuals, among whom 1,417 individuals

used a TCA at the moment of an ECG recording, we identified two independent loci (and one

suggestive locus) that modified the association between TCAs and RR intervals. The

significant loci were represented by variants rs6737205 (BRE) and rs9830388 (UBE2E2), and

the suggestive locus by variant rs11867129 (ABCA3). As it is well-known that the

anticholinergic activity of TCAs may increase heart rates and thus decrease the RR interval,

these findings may have a biological basis. Although the variance explained by our findings

was not calculated, it is likely modest in view of the low number of exposed individuals. The

three loci (BRE, UBE2E2, and ABCA3) either were excluded from the meta-analyses of

African and Hispanics/Latino ancestry participants (as part of quality control, e.g., DF <10)

or their effects were not nominally significant in the meta-analyses (Pinteraction >0.05). There

were no genome-wide significant loci that modified the association between TCA use and QT

intervals in cohorts of European and African American ancestry, although one locus had an

effect in Hispanic/Latino cohorts (TGFBR3, represented by rs2291477). None of the loci

previously observed to be associated with RR and QT intervals modified TCA effects on their

intervals. Furthermore, there was no multi-locus effect of variants previously associated with

heart rate or QT intervals on TCA-SNP interactions with RR or QT intervals.

Genetic variation in BRE has not been related to any study outcome in prior GWAS

reports. The association detected in our meta-analysis appeared to be driven by the estimate

observed in ASCOT, although the P-value for heterogeneity among studies was not

statistically significant. However, the directions of possible BRE effects were similar in the

other three studies in which the BRE variant passed quality control. ASCOT was the only

cohort that examined RR without ECGs, but this difference should have increased inter-

14

individual variation and would decrease statistical power. Therefore, future replication

studies of this variant are warranted.

Genetic variation in UBE2E2 was previously identified in GWAS reports on type 2

diabetes mellitus[38] and motion sickness[39]. To the best of our knowledge, no studies have

been published on genetic variation in UBE2E2 in relation to cardiac conduction (e.g., heart

rate or QT intervals) or pharmacological responses to medications. However, variant

rs9830388 was associated with mRNA expression levels of UBE2E2 in blood, based on

eQTL data[40 41]. Although significant in Europeans, the results on UBE2E2 were not

generalized in African and Hispanic/Latino ancestry cohorts, perhaps due to limited sample

sizes or the smaller effect size. Also, the linkage structure in this part of the genome may

differ among ethnicities, as seen in the regional plots.

Genetic variation in ABCA3, which produced a suggestive TCA-SNP interaction for

RR intervals, has been previously described in relation to the pharmacological response to

imatinib in chronic myeloid leukemia [42]. However, we did not find evidence in the

literature that ABCA3 is related to the electrophysiology of the heart.

Despite increasing our sample size and adding repeated ECG assessments to our

previous effort on this research topic[23], no significant TCA-SNP interactions were

identified for the QT interval in the meta-analysis of European cohorts (for which we had the

largest number of TCA users). The single locus with a significant effect in the

Hispanic/Latino meta-analysis (represented by rs2291477 in TGFBR3) did not have a

nominally significant effect among Europeans and African Americans. The observation may

suggest that TGFBR3 influences the QT interval specifically in Hispanic/Latino populations.

Alternatively, effects of the TCA-SNP interactions for QT intervals were too small (if any) to

be detected with the available sample size. Previously, this variant was identified in GWAS

in relation to optic disc morphology[43], but not cardiac conduction. Therefore, potential

15

explanations for the results of our study include lack of TCA-SNP interactions for QT

intervals or presence of only small effects that could not be detected, even with the large

sample size amassed in this study.

Earlier it was shown that TCA effects on QTc are related to the anticholinergic effects

of TCAs on heart rate [12]. When the QT interval was corrected for heart rate by methods

other than Bazett’s formula, the association between TCA use and QT intervals diminished.

Moreover, when using the statistical model adopted herein, the effect of TCAs on QT

intervals was shown to be negligible [12]. Furthermore, prescribed TCA doses and duration

of TCA use can be variable among individuals and cohorts. Such variability can increase

heterogeneity, making it more difficult to observe significant TCA-SNP interactions on QT

intervals. However, it remains possible that TCAs increase the QT interval duration in rare

cases (e.g., in association with low frequency genetic variants).

In addition, we showed that neither any genetic variants previously associated with a

higher resting heart rate or QT intervals, nor a multi-locus score of these variants

significantly modified the association between TCAs and resting RR or QT intervals[17 20].

The results of the multi-locus score analysis can be largely interpreted as a Mendelian

randomization analysis, estimating whether a high resting heart rate or prolonged QT interval

modified the TCA-SNP interactions. The results may indicate that participants with higher

resting heart rates or prolonged QT intervals are not at higher risk for further TCA-induced

increases in resting heart rate or QT duration. However, the variance explained by SNPs

associated with mainly resting heart rate is low (0.8-0.9%)[17], which could affect the

validity of this assumption.

A limitation of the study was the relatively low number of TCA users (especially in

non-European ancestry cohorts), although our study is the largest effort assessing TCA-SNP

interactions on RR and QT intervals to date. With limited power TCA interactions with

16

relatively low frequency SNPs or in small samples could have been missed. Attempts at

replication in non-European ancestry cohorts could have been underpowered. Second, our

study was unable to account for prescribed TCA dosages, duration of use, and treatment

adherence, which likely vary among cohorts from different countries. Such differences may

have resulted in heterogeneity among studies. Third, results on RR from the European

ancestry meta-analysis were not replicated in independent cohorts of a different ancestry.

Fourth, one cohort determined RR intervals from heart rates (ASCOT). This method would

increase RR measurement error, but independent of TCA exposures and genotypes. Any

misclassification would have led to findings in the direction of the zero hypothesis.

Furthermore, previously no heterogeneity in results was observed when comparing RR

intervals from ECGs and pulse recordings [17]. Fifth, we were not able to adjust for potential

confounders of SNP-TCA interactions. However, confounders with strong effects on the

drug-outcome association were shown to only modestly bias the results [44]. And last, TCA

doses may have been titrated to provide optimal blood concentrations, according to

participant CYP2D6 and CYP2C19 genotypes. Because we defined TCA exposures as present

or absent, SNP-TCA interactions caused by pharmacokinetic genes might have been missed.

For the present study, a well-powered, independent replication was not feasible, due

to the limited availability of populations with high quality ECGs (twelve-lead), reliable

assessment of drug use, and genome-wide SNP data. Our discovery effort should therefore be

viewed as hypothesis generating. Future studies should attempt to replicate and validate our

findings, to understand the pharmacological mechanisms involved.

In summary, we identified 2 independent genetic loci (BRE and UBE2E2) that modify

the association between TCAs and heart rate in populations of European ancestry, and 1 locus

that modifies the association between TCAs and QT intervals in Hispanic/Latino ancestry

17

populations. If replicated and validated, these results may provide new insights into

biological mechanisms underlying the effect of TCAs on heart rate.

18

Sources of Funding

Age, Gene/Environment Susceptibility – Reykjavik Study (AGES): This study has been

funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research

Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic

Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00-

063. The researchers are indebted to the participants for their willingness to participate in the

study.

Atherosclerosis Risk in Communities (ARIC): The Atherosclerosis Risk in Communities

Study is carried out as a collaborative study supported by National Heart, Lung and Blood

Institute Contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C,

HHSN268201100008C, HHSN268201100009C, HHSN268201100010C,

HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and

R01HL086694; National Human Genome Research Institute Contract U01HG004402; and

National Institutes of Health Contract HHSN268200625226C. We thank the staff and

participants of the ARIC study for their important contributions. Infrastructure was partly

supported by Grant No. UL1RR025005, a component of the National Institutes of Health and

NIH Roadmap for Medical Research. 

Anglo-Scandinavian Cardiac Outcomes Trial United Kingdom (ASCOT): This trial was

funded by an investigator-initiated grant from Pfizer, USA. The study was investigator led

and was conducted, analysed and reported independently of the company. Genotyping was

funded by the National Institutes for Health Research (NIHR) as part of the portfolio of

translational research of the NIHR Cardiovascular Biomedical Research Unit at Barts and the

London, QMUL, the NIHR Biomedical Research Centre at Imperial College, the

19

International Centre for Circulatory Health Charity and the Medical Research Council

through G952010. On behalf of the ASCOT investigators, we thank all ASCOT trial

participants, physicians, nurses, and practices in the participating countries for their important

contribution to the study. Helen R Warren, Mark J Caulfield and Patricia B Munrow wish to

acknowledge support from the NIHR Cardiovascular Biomedical Unit at Barts and the

London, Queen Mary University of London, UK.

Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts

HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079,

N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI

grants U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and

R01HL085251with additional contribution from the National Institute of Neurological

Disorders and Stroke (NINDS). Additional support was provided through R01AG023629

from the National Institute on Aging (NIA). A full list of principal CHS investigators and

institutions can be found at CHS-NHLBI.org. The provision of genotyping data was

supported in part by the National Center for Advancing Translational Sciences, CTSI grant

UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease

Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes

Endocrinology Research Center.

Framingham Heart Study (FHS):  FHS work was supported by the National Heart Lung and

Blood Institute of the National Institutes of Health and Boston University School of Medicine

(Contract No. N01-HC-25195 and Contract No. HHSN268201500001I), its contract with

Affymetrix for genotyping services (Contract No. N02-HL-6-4278), based on analyses by

FHS investigators participating in the SNP Health Association Resource (SHARe) project. A

20

portion of this research was conducted using the Linux Cluster for Genetic Analysis (LinGA-

II), funded by the Robert Dawson Evans Endowment of the Department of Medicine at

Boston University School of Medicine and Boston Medical Center. Additional support for

these analyses was provided by R01HL103612 (PI Psaty, subcontract PI, Vasan).

Measurement of the Gen 3 ECGs was supported by grants from the Doris Duke Charitable

Foundation and the Burroughs Wellcome Fund (Newton-Cheh) and the NIH (HL080025,

Newton-Cheh). 

Health, Aging, and Body Composition Study (Health ABC): This research was supported by

NIA Contracts N01AG62101, N01AG62103 and N01AG62106. The genome-wide

association study was funded by NIA Grant 1R01AG032098-01A1 to Wake Forest

University Health Sciences and genotyping services were provided by the Center for

Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the

National Institutes of Health to The Johns Hopkins University, Contract No.

HHSN268200782096C. This research was supported in part by the Intramural Research

Program of the NIH, National Institute on Aging. 

Hispanic Community Health Study/Study of Latinos (HCHS/SOL): We thank the participants

and staff of the HCHS/SOL study for their contributions to this study. The baseline

examination of HCHS/SOL was carried out as a collaborative study supported by contracts

from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North

Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of

Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State

University (N01-HC65237). The following Institutes/Centers/Offices contributed to the first

phase of HCHS/SOL through a transfer of funds to the NHLBI: National Institute on

21

Minority Health and Health Disparities, National Institute on Deafness and Other

Communication Disorders, National Institute of Dental and Craniofacial Research (NIDCR),

National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of

Neurological Disorders and Stroke, NIH Institution-Office of Dietary Supplements. The

Genetic Analysis Center at University of Washington was supported by NHLBI and NIDCR

contracts (HHSN268201300005C AM03 and MOD03). Genotyping efforts were supported

by NHLBI HSN 26220/20054C, NCATS CTSI grant UL1TR000124, and NIDDK Diabetes

Research Center (DRC) grant DK063491.

Jackson Heart Study (JHS): We thank the JHS participants and staff for their contributions to

this work. The JHS is supported by contracts HHSN268201300046C,

HHSN268201300047C, HSN268201300048C, HHSN268201300049C,

HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National

Institute on Minority Health and Health Disparities.

Multi-Ethnic Study of Atherosclerosis (MESA): This research was supported by contracts

HHSN2682015000031, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162,

N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-

HC-95168, N01-HC-95169 and by grants UL1-TR-000040, UL1-TR-001079, and UL1-RR-

025005 from NCRR. Funding for MESA Family was provided by grants R01-HL-071205,

R01-HL-071051, R01-HL-071250, R01-HL-071251, R01-HL-071252, R01-HL-071258, and

R01-HL-071259, and by UL1-RR-025005 and UL1RR033176 from NCRR. Funding for

MESA SHARe genotyping was provided by NHLBI Contract N02-HL-6-4278. The

provision of genotyping data was supported in part by the National Center for Advancing

Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and

22

Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the

Southern California Diabetes Endocrinology Research Center. Further information can be

found at http://www.mesa-nhlbi.org and

http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000209.v13.p3 .

Netherlands Epidemiology of Obesity (NEO): The authors of the NEO study thank all

individuals who participated in the Netherlands Epidemiology in Obesity study, all

participating general practitioners for inviting eligible participants and all research nurses for

collection of the data. We thank the NEO study group, Petra Noordijk and Ingeborg de Jonge

for the coordination, lab and data management of the NEO study. The genotyping in the NEO

study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-

Francois Deleuze.

Prospective Study of Pravastatin in the Elderly at Risk (PROSPER): The PROSPER study

was supported by an investigator initiated grant obtained from Bristol-Myers Squibb.

Professor Dr J W Jukema is an Established Clinical Investigator of the Netherlands Heart

Foundation (Grant No. 2001 D 032). Support for genotyping was provided by the seventh

framework program of the European commission (Grant No. 223004) and by the Netherlands

Genomics Initiative (Netherlands Consortium for Healthy Aging Grant 050-060-810). 

Rotterdam Study (RS): The RS is supported by the Erasmus Medical Center and Erasmus

University Rotterdam; The Netherlands Organization for Scientific Research; The

Netherlands Organization for Health Research and Development (ZonMw); the Research

Institute for Diseases in the Elderly; The Netherlands Heart Foundation; the Ministry of

Education, Culture and Science; the Ministry of Health Welfare and Sports; the European

23

Commission; and the Municipality of Rotterdam. Support for genotyping was provided by

The Netherlands Organization for Scientific Research (NWO) (175.010.2005.011,

911.03.012) and Research Institute for Diseases in the Elderly (RIDE). This study was

supported by The Netherlands Genomics Initiative (NGI)/Netherlands Organization for

Scientific Research (NWO) Project No. 050-060-810. This collaborative effort was supported

by an award from the National Heart, Lung and Blood Institute (R01-HL-103612, PI BMP).

CLA was supported in part by Grant R00-HL-098458 from the National Heart, Lung, and

Blood Institute.

Women’s Health Initiative (WHI): WHI program is funded by the National Heart, Lung, and

Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services

through contracts HHSN268201600018C, HHSN268201600001C,

HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C.

The authors thank the WHI investigators and staff for their dedication, and the study

participants for making the program possible. A full listing of WHI investigators can be

found at: http://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI

%20Investigator%20Long%20List.pdf

Women’s Health Initiative – GWAS of Treatment Response in Randomized Clinical Trials

(WHI GARNET): Within the Genomics and Randomized Trials Network, a GWAS of

Hormone Treatment and CVD and Metabolic Outcomes in the WHI was funded by the

National Human Genome Research Institute, National Institutes of Health, U.S. Department

of Health and Human Services through cooperative agreement U01HG005152 (Reiner). All

contributors to GARNET science are listed @ https://www.garnetstudy.org/Home. Dr.

Busch was supported in part by a grant from the National Cancer Institute (5T32CA009001).

24

Women’s Health Initiative – Modification of Particulate Matter-Mediated Arrythmogenesis

in Populations (WHI MOPMAP): The Modification of PM-Mediated Arrhythmogenesis in

Populations was funded by the National Institute of Environmental Health Sciences, National

Institutes of Health, U.S. Department of Health and Human Services through grant

R01ES017794 (Whitsel).

Women’s Health Initiative – The SNP Health Associations Resource (WHI SHARe): The

SNP Health Association Resource project was funded by the National Heart, Lung and Blood

Institute, National Institutes of Health, U.S. Department of Health and Human Services

through contract N02HL64278 (Kooperberg).

Disclosures

None.

25

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Figure legends

Figure 1: Genome-wide interaction analysis between tricyclic antidepressants and RR

interval sin European cohorts. Abbreviations: AGES, Age, Gene/Environment

Susceptibility – Reykjavik Study; ARIC, Atherosclerosis Risk in Communities Study;

ASCOT, Anglo-Scandinavian Cardiac Outcomes Trial; CHS, Cardiovascular Health Study;

FHS, Framingham Heart Study; GARNET, Genome-wide Association Research Network

into Effects of Treatment; HCHS/SOL, Hispanic Community Health Study/Study of Latinos;

Health ABC, Health, Aging, and Body Composition Study; JHS, Jackson Heart Study;

MESA, Multi-Ethnic Study of Atherosclerosis; MOPMAP, Modification of PM-Mediated

Arrhythmogenesis in Populations; NEO, Netherlands Epidemiology of Obesity; Nexposed,

number of independent participants using tricyclic antidepressants; PROSPER, Prospective

Study of Pravastatin in the Elderly at Risk; RS, Rotterdam Study; SD, standard deviation;

SHARe, WHI CT, Women’s Health Initiative Clinical Trials. A) –Log(p) plot of all SNPs

present in at least 3 European cohorts and passing all quality control steps. In black are all

SNPs within a 40 kb distance from the top result on chromosomes 2 and 3. B) Q-Q plot of the

meta-analysis in European cohorts. λ = 1.031. C) Cohort-specific and meta-analysis estimate

for rs6737205 on chromosome 2. Results are presented as the effect estimate of the

interaction between rs6737205 and TCA-use status on RR intervals (with the 95% confidence

interval). D) Cohort-specific and meta-analysis estimate for rs9830388 on chromosome 3.

Results are presented as the effect estimate of the interaction between rs9830388 and TCA-

use status on RR intervals (with the 95% confidence interval).

Figure 2: Genome-wide interaction analysis between tricyclic antidepressants and QT

intervals in European cohorts. A) –Log(p) plot of all SNPs present in at least 3 European

42

cohorts and passing all quality control steps. B) Q-Q plot of the meta-analysis in European

cohorts. λ = 1.022.

43

Table 1: Study characteristicsCohorts Nexposed Ntotal RR in ms,

mean (SD)QT in ms, mean (SD)

Age in years, mean (SD)

Females, % QTdef, % Beta blockers, %

Verapamil, %

Diltiazem, %

European AGES 67 1,976 938 (153) 406 (33.6) 74.6 (4.7) 64.2 3.0 13.7 1.7 3.5 ARIC 343 8,132 929 (138) 399 (28.8) 54.0 (5.7) 53.0 3.4 10.2 1.8 2.0 ASCOT 167 3,755 877 (153) -- 63.6 (8.1) 17.8 -- 45.1 0.3 1.5 CHS 165 2,893 953 (151) 414 (32.2) 72.1 (5.2) 62.8 3.3 10.8 3.4 3.3 FHS 56 3,168 981 (159) 414 (29.9) 40.2 (8.8) 39.5 0.3 3.5 NA NA Health ABC 43 1,442 952 (153) 414 (32.3) 73.7 (2.8) 49.4 3.5 13.8 3.5 5.7 MESA 55 2,384 977 (149) 412 (29.3) 62.3 (10.1) 52.1 0.9 7.1 0.3 0.1 NEO 84 5,366 940 (150) 406 (29.3) 55.9 (5.9) 47.0 1.4 12.6 0.3 0.2 PROSPER 151 4,555 932 (163) 414 (36.0) 75.3 (3.3) 46.6 2.6 20.2 2.1 5.4 RS1 85 4,805 875 (148) 397 (28.8) 68.8 (8.6) 60.2 2.6 15.3 0.7 1.6 RS2 31 1,889 886 (140) 406 (28.6) 65.0 (7.6) 56.6 2.3 15.5 0.3 1.2 RS3 23 1,950 881 (131) 401 (26.0) 56.0 (5.7) 54.1 1.1 10.5 0.1 0.4 WHI GARNET 60 1,391 928 (139) 401 (30.3) 65.0 (6.8) 100.0 1.4 11.4 2.4 2.1 WHI MOPMAP 87 2,000 929 (135) 402 (30.1) 63.0 (6.6) 100.0 0.9 13.3 1.9 1.9 Summary 1417 45,706 875-981 397-414 40.2-75.3 17.8-100 0.3-3.5 3.5-45.1 0.3-3.5 0.1-5.7African Americans ARIC 114 2,191 927 (151) 400 (32.8) 53.0 (5.8) 62.4 2.8 9.8 3.5 1.7 CHS 24 707 921 (166) 409 (35.3) 72.6 (5.6) 64.6 2.9 11.2 5.8 6.2 Health ABC 24 1,014 932 (143) 411 (34.8) 73.4 (2.9) 57.6 3.1 10.1 5.9 6.3 JHS 35 2,096 948 (150) 411 (30.1) 49.5 (11.8) 60.5 1.3 8.6 2.7 3.1 WHI CT SHARe 98 4,227 921 (149) 401 (33.8) 61.0 (6.8) 100.0 1.3 11.7 4.8 4.0 Summary 295 10,235 921-948 400-411 49.5-73.4 57.6-100 1.3-3.1 8.6-11.7 2.7-5.9 1.7-6.3Hispanic/Latinos WHI CT SHARe 41 1,784 928 (133) 402 (29.7) 60.0 (6.4) 100.0 1.0 8.2 2.5 1.8 HCHS/SOL 133 12,024 975 (143) 416 (28.4) 45.7 (13.8) 59.5 0.7 6.4 0.4 0.3 Summary 174 13,808 928-975 402-416 45.7-60.0 59.5-100 0.7-1.0 6.4-8.2 0.4-2.5 0.3-1.8Total summary 1886 69,749 875-981 397-416 40.2-75.3 17.8-100 0.3-3.5 3.5-45.1 0.3-5.9 0.1-6.3

Abbreviations: AGES, Age, Gene/Environment Susceptibility – Reykjavik Study; ARIC, Atherosclerosis Risk in Communities Study; ASCOT,

Anglo-Scandinavian Cardiac Outcomes Trial; CHS, Cardiovascular Health Study; FHS, Framingham Heart Study; GARNET, Genome-wide

44

Association Research Network into Effects of Treatment; HCHS/SOL, Hispanic Community Health Study/Study of Latinos; Health ABC,

Health, Aging, and Body Composition Study; JHS, Jackson Heart Study; MESA, Multi-Ethnic Study of Atherosclerosis; MOPMAP,

Modification of PM-Mediated Arrhythmogenesis in Populations; NA, not available. NEO, Netherlands Epidemiology of Obesity; Nexposed,

number of participants using tricyclic antidepressants; Ntotal, total number of participants contributing in the analysis; PROSPER, Prospective

Study of Pravastatin in the Elderly at Risk; RS, Rotterdam Study; SD, standard deviation; SHARe, Single nucleotide polymorphism (SNP)

Health Association Resource project; WHI CT, Women’s Health Initiative Clinical Trials.

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Table 2: Most significant independent loci in the genome-wide interaction analysis between tricyclic antidepressants and RR interval

SNP CHR Mapped gene Ethnicity EAF N studies Effect allele Betaint SEint Pint Phet

rs6737205 2 BRE EA 0.94 4 A 56.3 9.7 7.66e-9 0.16

AA Did not pass quality control

HSP Did not pass quality control

rs9830388 3 UBE2E2 EA 0.51 14 A 25.2 4.5 1.72e-8 0.44

AA 0.30 5 A 18.1 11.5 0.11 0.72

HSP 0.55 2 A -8.2 12.4 0.51 0.44

rs11867129 16 ABCA3 EA 0.09 11 T 35.7 6.9 2.57e-7 0.05

AA Did not pass quality control

HSP 0.41 2 T 4.5 13.5 0.74 0.12

Abbreviations: AA, African Americans; ABCA3, ATP binding cassette subfamily A member 3; BRE, brain and reproductive organ-expressed;

CHR, chromosome; EA, Europeans ; EAF, effective allele frequency; HSP, Hispanic/Latinos; N, number of studies included in the meta-

analysis after quality control. Phet, P-value for heterogeneity between studies; Pint, P-value of the interaction; SEint, standard error of the

interaction; UBE2E2, ubiquitin conjugating enzyme E2E 2. Only independent genetic variants with a P-value for interaction below 5e-7 are

displayed.

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